Precision Farming with Big Data Analytics

The future of farming will rely on big data to give farmers new insights into how they can grow crops more efficiently and sustainably.

With the Earth’s population approaching 8 billion, the UN estimates that global food production will need to increase by at least 60% to feed the world by 2050. It’s a daunting target. How do you dramatically and sustainably increase output in an uncertain world where natural resources are increasingly limited?

Naturally, the agri-food industry is turning to technology for the answer. Longer-term, the future of farming could well involve innovative vertical farms like Unit 84[1]. Sited in an industrial warehouse in Beckton, East London, 6,000 square feet of LED-lit growing space can produce more than 20,000 kilograms of salads and herbs a year, alongside a clever aquaculture solution that can produce 4,000 kilograms of fish.

Floating farms offer another alternative to the lack of extra farm land. Moored on the Hudson River, the Science Barge greenhouse in New York[2] is powered by renewable energy and grows tomatoes, melons, peppers and lettuce. Both urban approaches can operate 24 hours a day, 365 days a year. Crucially, they also use a fraction of the space, water and fertiliser compared to a traditional farm.

Realistically, these futuristic farms can’t yet operate at scale. So, to reach the UN’s 60% food target, the challenge is to make the most of the land we already have - improving crop yields, reducing waste and optimising supply chains. This is where big data has a pivotal role to play and, together with cloud computing technology, it’s already disrupting the agri-food industry and pointing the way forward.

Farmland in the UK is a finite resource – 17.5 million hectares covering 72% of the country.

A big data approach drives efficiency. It’s already helping to analyse decades of weather and crop records, looking for patterns in the numbers to enable farmers to more accurately predict crop yields. To this end, agri-tech companies like The Climate Corporation and Gamaya already offer data-driven agricultural insights that take soil type, seed suitability and local weather patterns into account.

Gamaya’s high-tech approach starts with a 40-band hyperspectral camera, which measures light reflected by plants as they grow. According to the company[3], plants with different physiology and characteristics reflect light differently, allowing for the early detection of disease, weed growth and nutrient deficiencies.

To reach the UN’s food target, farmers need to make the most of the land they already have.

Analytical software converts the hyperspectral data into actionable information, using machine learning and AI, “deployed on the farm using our hybrid cloud solution.” The resulting output is a set of detailed maps and models that encompass crop monitoring and yield prediction. Armed with this information, farmers can increase agricultural efficiency and optimise land use, the essence of what is known as ‘precision farming’.

In the UK, Agrimetrics bills itself as 'The world's first big data centre of excellence for the whole agri-food industry'. Amongst its many projects, it has established a Yield Enhancement Network (YEN) initiative to discover the precise factors that influence yield.

“The UK average yield for wheat is 8 tonnes a hectare,” Agrimetrics explains, “and has been for 20 years or more, but the 2015 AHDB variety trials showed that some farmers were able to achieve 16 tonnes per hectare… Is [this down to] the soil, the region, the weather, or farmer skill? What are individual decisions being made about the crop that really matter?”[4]

Of course, optimising land use isn’t the only way to increase agricultural efficiency. For years, plant genomics has enabled agricultural scientists to chemically engineer seeds using big data to make them grow faster, stronger, more resistant to disease and adaptable to different environments.

Take soybeans, for example. “Soybeans are increasingly relied upon as a source of oil and protein across the globe, both for animal and human use,” says NRGene, a cloud-based genomics big-data solution provider with applications for plant and animal breeding.[5]

“With the knowledge delivered by the increasing number of comprehensive genome assemblies, scientists can use traditional breeding to increase simultaneously the oil and protein volume and nutritional value produced within a single plant.”

Grow faster, stronger and more adaptable with big data analytics.

Growing more food per hectare is only one part of the solution. Improvements can also be made in the supply chain through automation and reducing waste.

Drones are currently used for mapping and monitoring, but they might eventually be used for planting and spraying.

According to Agrimetrics, “estimates from the Food and Agriculture Organisation of the United Nations suggest that globally [food waste] could be between 500 and 850 million tonnes, equivalent to approximately 10-15% of the world’s food production… The volumes in the UK could be immense. One fenland producer estimates 3 million iceberg lettuce heads are produced surplus to requirements each year.”[6]

In a joint project supported by Agrimetrics and the National Institute of Agricultural Botany (NIAB), Asda’s potato growers have started using smartphones to document the progress of their crops throughout the season. These photos are subsequently uploaded into analytical software, which gauges the crop’s potential in relation to historical data and weather.

A predictive yield report can then be produced, allowing the stakeholders to “make accurate decisions earlier in the season that will reduce the risk of gluts or shortages, at farm and retail level.” While farmers can use the report to “improve management of the crop in subsequent years to produce higher marketable yields.”

Of course, this new trend for precision farming relies on accurate data capture. Technology has an increasing role to play here too as satellites and UAVs provide high-resolution imaging data in visible, near-infrared, thermal infrared, and microwave wavelengths of light.

Moving beyond surveillance/crop monitoring tasks, drones will be able to deploy pesticides with precision or even be used to plant crops. Oxford-based BioCarbon Engineering is currently working on a system that plants trees from the air, shooting seed pods into the ground from a hovering UAV.

Beyond that, expect to see GPS-controlled autonomous tractors and fleets of agricultural bots rolling across our fields. The Hands Free Hectare project in Shropshire is a glimpse into this automated and robotic future, “growing the first arable crop remotely, without operators in the driving seats or agronomists on the ground.”[7]

While the technology you can see (like drones and robots) certainly sounds exciting, it’s the technology you can’t see that will surely have the greatest impact. Behind the scenes, big data is driving an agricultural evolution that aims to give farmers more control over their land and more information about its use, all while increasing efficiency and reducing costs.

Big data has the potential to deliver big insights for business. But only if that data is clean. Processing the wrong data will lead you to the wrong answer.

Click here for your free power of data cleaning guide

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